Contoh

Memanggil Gemini dengan Chat Completions API

Contoh berikut menunjukkan cara mengirim permintaan non-streaming:

REST

  curl -X POST \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "Content-Type: application/json" \
  https://${LOCATION}-aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/${LOCATION}/endpoints/openapi/chat/completions \
  -d '{
    "model": "google/${MODEL_ID}",
    "messages": [{
      "role": "user",
      "content": "Write a story about a magic backpack."
    }]
  }'

Python

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Python di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Python Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal. 

from google.auth import default
import google.auth.transport.requests

import openai

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
# location = "us-central1"

# Programmatically get an access token
credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
credentials.refresh(google.auth.transport.requests.Request())

# OpenAI Client
client = openai.OpenAI(
    base_url=f"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/endpoints/openapi",
    api_key=credentials.token,
)

response = client.chat.completions.create(
    model="google/gemini-2.0-flash-001",
    messages=[{"role": "user", "content": "Why is the sky blue?"}],
)

print(response)

Contoh berikut menunjukkan cara mengirim permintaan streaming ke model Gemini menggunakan Chat Completions API:

REST

  curl -X POST \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "Content-Type: application/json" \
  https://${LOCATION}-aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/${LOCATION}/endpoints/openapi/chat/completions \
  -d '{
    "model": "google/${MODEL_ID}",
    "stream": true,
    "messages": [{
      "role": "user",
      "content": "Write a story about a magic backpack."
    }]
  }'

Python

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Python di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Python Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal. 

from google.auth import default
import google.auth.transport.requests

import openai

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
# location = "us-central1"

# Programmatically get an access token
credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
credentials.refresh(google.auth.transport.requests.Request())

# OpenAI Client
client = openai.OpenAI(
    base_url=f"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/endpoints/openapi",
    api_key=credentials.token,
)

response = client.chat.completions.create(
    model="google/gemini-2.0-flash-001",
    messages=[{"role": "user", "content": "Why is the sky blue?"}],
    stream=True,
)
for chunk in response:
    print(chunk)

Mengirim perintah dan gambar ke Gemini API di Vertex AI

Python

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Python di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Python Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal. 


from google.auth import default
import google.auth.transport.requests

import openai

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
# location = "us-central1"

# Programmatically get an access token
credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
credentials.refresh(google.auth.transport.requests.Request())

# OpenAI Client
client = openai.OpenAI(
    base_url=f"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/endpoints/openapi",
    api_key=credentials.token,
)

response = client.chat.completions.create(
    model="google/gemini-2.0-flash-001",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Describe the following image:"},
                {
                    "type": "image_url",
                    "image_url": "gs://cloud-samples-data/generative-ai/image/scones.jpg",
                },
            ],
        }
    ],
)

print(response)

Memanggil model yang di-deploy sendiri dengan Chat Completions API

Contoh berikut menunjukkan cara mengirim permintaan non-streaming:

REST

  curl -X POST \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "Content-Type: application/json" \
  https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/global/endpoints/${ENDPOINT}/chat/completions \
  -d '{
    "messages": [{
      "role": "user",
      "content": "Write a story about a magic backpack."
    }]
  }'

Python

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Python di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Python Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal. 

from google.auth import default
import google.auth.transport.requests

import openai

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
# location = "us-central1"
# model_id = "gemma-2-9b-it"
# endpoint_id = "YOUR_ENDPOINT_ID"

# Programmatically get an access token
credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
credentials.refresh(google.auth.transport.requests.Request())

# OpenAI Client
client = openai.OpenAI(
    base_url=f"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/endpoints/{endpoint_id}",
    api_key=credentials.token,
)

response = client.chat.completions.create(
    model=model_id,
    messages=[{"role": "user", "content": "Why is the sky blue?"}],
)
print(response)

Contoh berikut menunjukkan cara mengirim permintaan streaming ke model yang di-deploy sendiri menggunakan Chat Completions API:

REST

    curl -X POST \
      -H "Authorization: Bearer $(gcloud auth print-access-token)" \
      -H "Content-Type: application/json" \
    https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/global/endpoints/${ENDPOINT}/chat/completions \
    -d '{
      "stream": true,
      "messages": [{
        "role": "user",
        "content": "Write a story about a magic backpack."
      }]
    }'

Python

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Python di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Python Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal. 

from google.auth import default
import google.auth.transport.requests

import openai

# TODO(developer): Update and un-comment below lines
# project_id = "PROJECT_ID"
# location = "us-central1"
# model_id = "gemma-2-9b-it"
# endpoint_id = "YOUR_ENDPOINT_ID"

# Programmatically get an access token
credentials, _ = default(scopes=["https://www.googleapis.com/auth/cloud-platform"])
credentials.refresh(google.auth.transport.requests.Request())

# OpenAI Client
client = openai.OpenAI(
    base_url=f"https://{location}-aiplatform.googleapis.com/v1/projects/{project_id}/locations/{location}/endpoints/{endpoint_id}",
    api_key=credentials.token,
)

response = client.chat.completions.create(
    model=model_id,
    messages=[{"role": "user", "content": "Why is the sky blue?"}],
    stream=True,
)
for chunk in response:
    print(chunk)

extra_body contoh

Anda dapat menggunakan SDK atau REST API untuk meneruskan extra_body.

Tambahkan thought_tag_marker

{
  ...,
  "extra_body": {
     "google": {
       ...,
       "thought_tag_marker": "..."
     }
   }
}

Menambahkan extra_body menggunakan SDK

client.chat.completions.create(
  ...,
  extra_body = {
    'extra_body': { 'google': { ... } }
  },
)

extra_content contoh

Anda dapat mengisi kolom ini menggunakan REST API secara langsung.

extra_content dengan string content

{
  "messages": [
    { "role": "...", "content": "...", "extra_content": { "google": { ... } } }
  ]
}

extra_content per pesan

{
  "messages": [
    {
      "role": "...",
      "content": [
        { "type": "...", ..., "extra_content": { "google": { ... } } }
      ]
    }
}

Panggilan per alat extra_content

{
  "messages": [
    {
      "role": "...",
      "tool_calls": [
        {
          ...,
          "extra_content": { "google": { ... } }
        }
      ]
    }
  ]
}

Contoh permintaan curl

Anda dapat menggunakan permintaan curl ini secara langsung, bukan melalui SDK.

Gunakan thinking_config dengan extra_body

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  https://us-central1-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/us-central1/endpoints/openapi/chat/completions \
  -d '{ \
    "model": "google/gemini-2.5-flash-preview-04-17", \
    "messages": [ \
      { "role": "user", \
      "content": [ \
        { "type": "text", \
          "text": "Are there any primes number of the form n*ceil(log(n))" \
        }] }], \
    "extra_body": { \
      "google": { \
          "thinking_config": { \
          "include_thoughts": true, "thinking_budget": 10000 \
        }, \
        "thought_tag_marker": "think" } }, \
    "stream": true }'

Gunakan stream_function_call_arguments

Contoh permintaan:

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  https://aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/global/endpoints/openapi/chat/completions \
  -d '{
  "model": "google/gemini-3.1-pro-preview", \
  "messages": [ \
    { "role": "user", "content": "What is the weather like in Boston and New Delhi today?" } ], \
  "tools": [ \
    { \
      "type": "function", \
      "function": { \
        "name": "get_current_weather", \
        "description": "Get the current weather in a given location", \
        "parameters": { \
          "type": "object", \
          "properties": { \
            "location": { \
              "type": "string", \
              "description": "The city and state, e.g. San Francisco, CA" \
            }, \
            "unit": { \
              "type": "string", \
              "enum": [ \
                "celsius", \
                "fahrenheit" \
              ] \
            } \
          }, \
          "required": [ \
            "location", \
            "unit" \
          ] \
        } \
      } \
    } \
  ], \
  "extra_body": { \
    "google": { \
      "stream_function_call_arguments": true \
    } \
  }, \
  "stream": true \
}'

Contoh respons:

data: {"choices":[{"delta":{"role":"assistant","tool_calls":[{"extra_content":{"google":{"thought_signature":"..."}},"function":{"arguments":"","name":"get_current_weather"},"id":"function-call-c855348a-459a-46a4-a8ad-aa0a4e7c3563","index":1,"type":"function"}]},"index":0,"logprobs":null}],"created":1770850461,"id":"nQiNafGyF5rw998PstqooAY","model":"google/gemini-3.1-pro-preview","object":"chat.completion.chunk","system_fingerprint":""}

data: {"choices":[{"delta":{"role":"assistant","tool_calls":[{"function":{"arguments":"{\"location\":\"Boston, MA","name":"get_current_weather"},"id":"function-call-c855348a-459a-46a4-a8ad-aa0a4e7c3563","index":0,"type":"function"}]},"index":0,"logprobs":null}],"created":1770850461,"id":"nQiNafGyF5rw998PstqooAY","model":"google/gemini-3.1-pro-preview","object":"chat.completion.chunk","system_fingerprint":""}

data: {"choices":[{"delta":{"role":"assistant","tool_calls":[{"function":{"arguments":"\"","name":"get_current_weather"},"id":"function-call-c855348a-459a-46a4-a8ad-aa0a4e7c3563","index":0,"type":"function"}]},"index":0,"logprobs":null}],"created":1770850461,"id":"nQiNafGyF5rw998PstqooAY","model":"google/gemini-3.1-pro-preview","object":"chat.completion.chunk","system_fingerprint":""}

data: {"choices":[{"delta":{"role":"assistant","tool_calls":[{"function":{"arguments":",\"unit\":\"celsius","name":"get_current_weather"},"id":"function-call-c855348a-459a-46a4-a8ad-aa0a4e7c3563","index":0,"type":"function"}]},"index":0,"logprobs":null}],"created":1770850461,"id":"nQiNafGyF5rw998PstqooAY","model":"google/gemini-3.1-pro-preview","object":"chat.completion.chunk","system_fingerprint":""}

data: {"choices":[{"delta":{"role":"assistant","tool_calls":[{"function":{"arguments":"\"","name":"get_current_weather"},"id":"function-call-c855348a-459a-46a4-a8ad-aa0a4e7c3563","index":0,"type":"function"}]},"index":0,"logprobs":null}],"created":1770850461,"id":"nQiNafGyF5rw998PstqooAY","model":"google/gemini-3.1-pro-preview","object":"chat.completion.chunk","system_fingerprint":""}

data: {"choices":[{"delta":{"role":"assistant","tool_calls":[{"function":{"arguments":"}","name":"get_current_weather"},"id":"function-call-c855348a-459a-46a4-a8ad-aa0a4e7c3563","index":0,"type":"function"}]},"index":0,"logprobs":null}],"created":1770850461,"id":"nQiNafGyF5rw998PstqooAY","model":"google/gemini-3.1-pro-preview","object":"chat.completion.chunk","system_fingerprint":""}

data: {"choices":[{"delta":{"role":"assistant","tool_calls":[{"function":{"arguments":"","name":"get_current_weather"},"id":"function-call-df0d087c-ad74-46f1-ba4a-9353cbf288a8","index":0,"type":"function"}]},"index":0,"logprobs":null}],"created":1770850461,"id":"nQiNafGyF5rw998PstqooAY","model":"google/gemini-3.1-pro-preview","object":"chat.completion.chunk","system_fingerprint":""}

data: {"choices":[{"delta":{"role":"assistant","tool_calls":[{"function":{"arguments":"{\"location\":\"New Delhi, India","name":"get_current_weather"},"id":"function-call-df0d087c-ad74-46f1-ba4a-9353cbf288a8","index":1,"type":"function"}]},"index":0,"logprobs":null}],"created":1770850461,"id":"nQiNafGyF5rw998PstqooAY","model":"google/gemini-3.1-pro-preview","object":"chat.completion.chunk","system_fingerprint":""}

data: {"choices":[{"delta":{"role":"assistant","tool_calls":[{"function":{"arguments":"\"","name":"get_current_weather"},"id":"function-call-df0d087c-ad74-46f1-ba4a-9353cbf288a8","index":1,"type":"function"}]},"index":0,"logprobs":null}],"created":1770850461,"id":"nQiNafGyF5rw998PstqooAY","model":"google/gemini-3.1-pro-preview","object":"chat.completion.chunk","system_fingerprint":""}

data: {"choices":[{"delta":{"role":"assistant","tool_calls":[{"function":{"arguments":",\"unit\":\"celsius","name":"get_current_weather"},"id":"function-call-df0d087c-ad74-46f1-ba4a-9353cbf288a8","index":1,"type":"function"}]},"index":0,"logprobs":null}],"created":1770850461,"id":"nQiNafGyF5rw998PstqooAY","model":"google/gemini-3.1-pro-preview","object":"chat.completion.chunk","system_fingerprint":""}

data: {"choices":[{"delta":{"role":"assistant","tool_calls":[{"function":{"arguments":"\"","name":"get_current_weather"},"id":"function-call-df0d087c-ad74-46f1-ba4a-9353cbf288a8","index":1,"type":"function"}]},"index":0,"logprobs":null}],"created":1770850461,"id":"nQiNafGyF5rw998PstqooAY","model":"google/gemini-3.1-pro-preview","object":"chat.completion.chunk","system_fingerprint":""}

data: {"choices":[{"delta":{"role":"assistant","tool_calls":[{"function":{"arguments":"}","name":"get_current_weather"},"id":"function-call-df0d087c-ad74-46f1-ba4a-9353cbf288a8","index":1,"type":"function"}]},"finish_reason":"tool_calls","index":0,"logprobs":null}],"created":1770850461,"id":"nQiNafGyF5rw998PstqooAY","model":"google/gemini-3.1-pro-preview","object":"chat.completion.chunk","system_fingerprint":"","usage":{"completion_tokens":45,"completion_tokens_details":{"reasoning_tokens":504},"extra_properties":{"google":{"traffic_type":"PROVISIONED_THROUGHPUT"}},"prompt_tokens":27,"total_tokens":576}}

data: [DONE]

Pembuatan gambar

Agar tetap kompatibel dengan format respons OpenAI, kolom audio respons diisi secara eksplisit dengan extra_content.google.mime_type yang menunjukkan jenis MIME hasil.

Contoh permintaan:

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  https://aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/global/endpoints/openapi/chat/completions \
  -d '{"model":"google/gemini-3-pro-image-preview", "messages":[{ "role": "user", "content": "Generate an image of a cat." }], "modalities": ["image"] }'

Contoh respons:

{
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "logprobs": null,
      "message": {
        "audio": {
          "data": "<BASE64_BYTES>",
          "extra_content": {
            "google": {
              "mime_type": "image/png"
            }
          }
        },
        "content": null,
        "extra_content": {
          "google": {
            "thought_signature": "..."
          }
        },
        "role": "assistant"
      }
    }
  ],
  "created": 1770850692,
  "id": "hAmNaZb8BZOX4_UPlNXoEA",
  "model": "google/gemini-3-pro-image-preview",
  "object": "chat.completion",
  "system_fingerprint": "",
  "usage": {
    "completion_tokens": 1120,
    "completion_tokens_details": {
      "reasoning_tokens": 251
    },
    "extra_properties": {
      "google": {
        "traffic_type": "PROVISIONED_THROUGHPUT"
      }
    },
    "prompt_tokens": 7,
    "total_tokens": 1378
  }
}

Permintaan multimodal

Chat Completions API mendukung berbagai input multimodal, termasuk audio dan video.

Menggunakan image_url untuk meneruskan data gambar

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  https://us-central1-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/us-central1/endpoints/openapi/chat/completions \
  -d '{ \
    "model": "google/gemini-2.0-flash-001", \
    "messages": [{ "role": "user", "content": [ \
      { "type": "text", "text": "Describe this image" }, \
      { "type": "image_url", "image_url": "gs://cloud-samples-data/generative-ai/image/scones.jpg" }] }] }'

Menggunakan input_audio untuk meneruskan data audio

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  https://us-central1-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/us-central1/endpoints/openapi/chat/completions \
  -d '{ \
    "model": "google/gemini-2.0-flash-001", \
    "messages": [ \
      { "role": "user", \
        "content": [ \
          { "type": "text", "text": "Describe this: " }, \
          { "type": "input_audio", "input_audio": { \
            "format": "audio/mp3", \
            "data": "gs://cloud-samples-data/generative-ai/audio/pixel.mp3" } }] }] }'

Respons fungsi multi-modal

Contoh permintaan:

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  https://aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/global/endpoints/openapi/chat/completions \
  -d '{ \
    "model": "google/gemini-3.1-pro-preview", \
    "messages": [ \
      { "role": "user", "content": "Show me the green shirt I ordered last month." }, \
      { \
        "role": "assistant", \
        "tool_calls": [ \
          { \
            "extra_content": { \
              "google": { \
                "thought_signature": "<THOUGHT_SIGNATURE>" \
              } \
            }, \
            "function": { \
              "arguments": "{\"item_name\":\"green shirt\"}", \
              "name": "get_image" \
            }, \
            "id": "function-call-a350228d-0283-4792-8bfa-40da064fb959", \
            "type": "function" \
          } \
        ] \
      }, \
      { \
        "role": "tool", \
        "tool_call_id": "function-call-a350228d-0283-4792-8bfa-40da064fb959", \
        "content": "{\"image_ref\":{\"$ref\":\"dress.jpg\"}}", \
        "extra_content": { \
          "google": { \
            "parts": [ \
              { \
                "file_data": { \
                  "mime_type": "image/jpg", \
                  "display_name": "dress.jpg", \
                  "file_uri": "gs://cloud-samples-data/generative-ai/image/dress.jpg" \
                } \
              } \
            ] \
          } \
        } \
      } \
    ], \
    "tools": [ \
      { \
        "type": "function", \
        "function": { \
          "name": "get_image", \
          "description": "Retrieves the image file reference for a specific order item.", \
          "parameters": { \
            "type": "object", \
            "properties": { \
              "item_name": { \
                "type": "string", \
                "description": "The name or description of the item ordered (e.g., 'green shirt')." \
              } \
            }, \
            "required": [ \
              "item_name" \
            ] \
          } \
        } \
      } \
    ] \
  }'

Contoh respons:

{
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "logprobs": null,
      "message": {
        "content": "Here is the image of the green shirt you ordered.",
        "role": "assistant"
      }
    }
  ],
  "created": 1770852204,
  "id": "bA-NacCPKoae_9MPsNCn6Qc",
  "model": "google/gemini-3.1-pro-preview",
  "object": "chat.completion",
  "system_fingerprint": "",
  "usage": {
    "completion_tokens": 16,
    "extra_properties": {
      "google": {
        "traffic_type": "ON_DEMAND"
      }
    },
    "prompt_tokens": 1139,
    "total_tokens": 1155
  }
}

Output terstruktur

Anda dapat menggunakan parameter response_format untuk mendapatkan output terstruktur.

Contoh penggunaan SDK

from pydantic import BaseModel
from openai import OpenAI

client = OpenAI()

class CalendarEvent(BaseModel):
    name: str
    date: str
    participants: list[str]

completion = client.beta.chat.completions.parse(
    model="google/gemini-2.5-flash-preview-04-17",
    messages=[
        {"role": "system", "content": "Extract the event information."},
        {"role": "user", "content": "Alice and Bob are going to a science fair on Friday."},
    ],
    response_format=CalendarEvent,
)

print(completion.choices[0].message.parsed)

Menggunakan endpoint global dalam mode yang kompatibel dengan OpenAI

Contoh berikut menunjukkan cara menggunakan endpoint global dalam mode yang kompatibel dengan OpenAI:

REST

  curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
  https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/global/endpoints/openapi/chat/completions\
  -d '{ \
    "model": "google/gemini-2.0-flash-001", \
    "messages": [ \
    {"role": "user", \
      "content": "Hello World" \
      }] \
      }'

Langkah berikutnya